Lijun Gong

TL;DR: Zhang et al. as mentioned in this paper used a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes, and the network identifies the mask that maintains the most robust features of the target objects over a long temporal span.

TL;DR: This paper proposes the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network, and applies residual learning to take appearance changes into account to reduce model degradation during online update.

TL;DR: In this paper, the discriminative correlation filters (DCFs) are reformulated as a one-layer convolutional neural network for an end-to-end training.

TL;DR: This paper proposes a facial component guided deep Convolutional Neural Network to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image.

TL;DR: Zhang et al. as discussed by the authors used a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes, and the network identifies the mask that maintains the most robust features of the target objects over a long temporal span.